How to Use the Amplemarket MCP in Pydantic AI

Build type-safe sales workflows using Pydantic AI to validate Amplemarket data at runtime.

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Connect Amplemarket MCP to Pydantic AI

Create your Vinkius account to connect Amplemarket to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Amplemarket with Pydantic AI

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Validate sales intelligence using Pydantic AI

Your agent uses the MCP interface to fetch firmographic details with the `enrich_company` tool and parses them directly into strict Python types. If the API returns a malformed domain or an unexpected null value, the framework raises a validation error immediately. This strict runtime checking prevents your agent from passing bad records to your CRM. You write clean code knowing that every company size, funding round, and industry tag matches your schema exactly.

Extract verified contacts without silent errors

Fetching target details requires absolute accuracy, which you get by combining `get_contact` and `enrich_person` within your type-safe agent. The server returns structured contact payloads that the client validates against your defined Pydantic models. If a prospect record lacks a critical field like a verified email address, the run fails loudly. This mechanism ensures your outbound engine never attempts to draft emails to broken or incomplete addresses.

Inspect active campaigns and exclusions safely

Your agent checks campaign statuses using `list_sequences` and `list_exclusions` to keep your outreach clean. The output from these tools is validated at runtime, ensuring your blocklists are parsed without missing records. Using these clean datasets, the agent determines if a prospect is safe to contact. Because the data is guaranteed to match your types, your agent avoids making logical errors when checking exclusion lists.

Setup guide

Set up Amplemarket MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "amplemarket-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Amplemarket tools.",
)

result = await agent.run("List recent Amplemarket transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Amplemarket. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Amplemarket MCP in Pydantic AI

Install the slim package with MCP support and use the unified MCPToolset class. Point it to your Vinkius HTTP endpoint and pass the toolset directly to your agent constructor.

The framework will throw a validation error at runtime instead of silently passing bad data. This allows you to catch API changes or empty fields before they corrupt your sales database.

Yes. The framework fully supports async execution, meaning your agent can check `list_contacts` and `list_exclusions` in parallel to speed up your pipeline.

No. The MCP Server automatically exposes schemas for tools like `enrich_person`, which the client uses to validate arguments and return values out of the box.

All validation happens locally in your Python runtime, while the server runs inside an ephemeral, zero-trust V8 isolate. Your company records and email lists are never cached or exposed to external networks.

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